Recent recommender systems have achieved high precision in recommending favorite items to users. However, it has been reported that user satisfaction does not necessarily increase even when a recommender system recommends items high precision. User satisfaction is considered to be influenced by many factors. Among these factors, we focus in particular on user intervention. User intervention is a user control over a recommender system. We provide three hypotheses: i) user intervention in the recommendation process itself improves the user satisfaction, ii) user intervention improves the user satisfaction when the intervention is reflected in the recommendation results, and iii) the degree of improvement in user satisfaction differs among the types of user interventions applied. In this study, we conducted a user experiment using several kinds of interventions, and clarify the relationship between user intervention and user satisfaction.